@inproceedings{sun-etal-2025-docspiral,
    title = "{D}oc{S}piral: A Platform for Integrated Assistive Document Annotation through Human-in-the-Spiral",
    author = "Sun, Qiang  and
      Li, Sirui  and
      Bi, Tingting  and
      Huynh, Du Q.  and
      Reynolds, Mark  and
      Luo, Yuanyi  and
      Liu, Wei",
    editor = "Mishra, Pushkar  and
      Muresan, Smaranda  and
      Yu, Tao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-demo.26/",
    pages = "267--274",
    ISBN = "979-8-89176-253-4",
    abstract = "Acquiring structured data from domain-specific, image-based documents{---}such as scanned reports{---}is crucial for many downstream tasks but remains challenging due to document variability. Many of these documents exist as images rather than as machine-readable text, which requires human annotation to train automated extraction systems.We present \textbf{DocSpiral}, the first Human-in-the-Spiral assistive document annotation platform, designed to address the challenge of extracting structured information from domain-specific, image-based document collections.Our spiral design establishes an iterative cycle in which human annotations train models that progressively require less manual intervention. \textbf{DocSpiral} integrates document format normalization, comprehensive annotation interfaces, evaluation metrics dashboard, and API endpoints for the development of AI / ML models into a unified workflow.Experiments demonstrate that our framework reduces annotation time by at least 41{\%} while showing consistent performance gains across three iterations during model training.By making this annotation platform freely accessible, we aim to lower barriers to AI/ML models development in document processing, facilitating the adoption of large language models in image-based, document-intensive fields such as geoscience and healthcare. The system is freely available at: \url{https://app.ai4wa.com}. The demonstration video is available: \url{https://app.ai4wa.com/docs/docspiral/demo}."
}